NLP and Computer Vision with Deep Learning Research Seminar (Wintersemester 2023/2024)
Dozent:
Prof. Dr. Gerard de Melo
(Artificial Intelligence and Intelligent Systems)
,
Jingyi Zhang
(Artificial Intelligence and Intelligent Systems)
Website zum Kurs:
https://moodle.hpi.de/course/view.php?id=670
Allgemeine Information
- Semesterwochenstunden: 4
- ECTS: 6
- Benotet:
Ja
- Einschreibefrist: 01.10.2023 - 31.10.2023
- Prüfungszeitpunkt §9 (4) BAMA-O: 22.12.2023
- Lehrform: Projektseminar
- Belegungsart: Wahlpflichtmodul
- Lehrsprache: Englisch
Studiengänge, Modulgruppen & Module
- ISAE: Internet, Security & Algorithm Engineering
- HPI-ISAE-K Konzepte und Methoden
- ISAE: Internet, Security & Algorithm Engineering
- HPI-ISAE-T Techniken und Werkzeuge
- ISAE: Internet, Security & Algorithm Engineering
- HPI-ISAE-S Spezialisierung
- OSIS: Operating Systems & Information Systems Technology
- HPI-OSIS-K Konzepte und Methoden
- OSIS: Operating Systems & Information Systems Technology
- HPI-OSIS-T Techniken und Werkzeuge
- OSIS: Operating Systems & Information Systems Technology
- HPI-OSIS-S Spezialisierung
- DANA: Data Analytics
- HPI-DANA-K Konzepte und Methoden
- DANA: Data Analytics
- HPI-DANA-T Techniken und Werkzeuge
- DANA: Data Analytics
- HPI-DANA-S Spezialisierung
- CODS: Complex Data Systems
- HPI-CODS-K Konzepte und Methoden
- CODS: Complex Data Systems
- HPI-CODS-T Techniken und Werkzeuge
- CODS: Complex Data Systems
- HPI-CODS-S Spezialisierung
- DICR: Digitalization of Clinical and Research Processes
- HPI-DICR-C Concepts and Methods
- DICR: Digitalization of Clinical and Research Processes
- HPI-DICR-T Technologies and Tools
- DICR: Digitalization of Clinical and Research Processes
- HPI-DICR-S Specialization
- APAD: Acquisition, Processing and Analysis of Health Data
- HPI-APAD-C Concepts and Methods
- APAD: Acquisition, Processing and Analysis of Health Data
- HPI-APAD-T Technologies and Tools
- APAD: Acquisition, Processing and Analysis of Health Data
- HPI-APAD-S Specialization
- DSYS: Data-Driven Systems
- HPI-DSYS-C Concepts and Methods
- DSYS: Data-Driven Systems
- HPI-DSYS-T Technologies and Tools
- DSYS: Data-Driven Systems
- HPI-DSYS-S Specialization
- MALA: Machine Learning and Analytics
- HPI-MALA-C Concepts and Methods
- MALA: Machine Learning and Analytics
- HPI-MALA-T Technologies and Tools
- MALA: Machine Learning and Analytics
- HPI-MALA-S Specialization
Beschreibung
Deep Learning is the foundation for most modern approaches to AI, especially for natural language processing (NLP) and computer vision (CV). Recent advances such as Transformers, CLIP, ChatGPT show the effectivess of deep learning solutions in tackling many complex single modality and multimodal problems.
This seminar aims to build upon recent research in NLP, CV, and deep learning.
Potential topics that could be explored in this seminar include Transformer models for NLP, vision–and–language models, startup success prediction, etc.
Voraussetzungen
The main focus is on research, so depending on the topic some prior familiarity with ML/AI, especially Deep Learning, is probably needed. For many topics, you will need some prior experience with either PyTorch, Tensorflow, or Jax, and prior experience in training deep neural networks with GPUs. For example, you can take our "Natural Language Processing" course, Christoph Lippert's Deep Learning course, or Dagmar Kainmüller's computer vision course to acquire the prerequisite knowledge.
Lern- und Lehrformen
This seminar focuses on practical research skills. Depending on the topic, you can either investigate it alone or in a team. Students will work on these projects throughout the semester, supported by weekly meetings with their mentor.
Leistungserfassung
The grade will be based on the following:
- 25% Final Presentation
- 75% Project (7 to 10-Page Paper and Code Submission)
Important criteria for the evaluation of the project include the project effort, the quality of the paper, and the reproducibility of the code. Further details will be given during the seminar.
Termine
Potential topics are presented on the first day (Tuesday, Oct. 17, 15:15-16:45 in HPI Building K, room K-2.04).
There will be a mid-term presentation during the semester in addition to a final presentation at the end of the semester.
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